Economic Quarterly Volume 100, Number 1 First Quarter 2014 Pages 2350

The Financial Crisis, theCollapse of Bank Entry, andChanges in the SizeDistribution of Banks

Roisin McCord and Edward Simpson Prescott

The recent nancial crisis has had an enormous impact on thebanking industry. There were numerous bank failures, bankbailouts, and bank mergers. One of the more striking e¤ects

was the decline in the number of banks. At the end of 2007, as the re-cent nancial crisis was developing, there were 6,153 commercial banksin the United States. At the end of 2013, as the direct e¤ects of thecrisis were wearing o¤, the number of banks had dropped 14 percent,reaching 5,317.1

The purpose of this article is to document the size and scope of theserecent changes to the size distribution of banks, particularly among thesmaller banks, and explain the sources of these recent changes. In doingso, we also update the work of Janicki and Prescott (2006), who studiedthe size distribution in the banking industry from 19602005.

Our most signicant nding is that the recent decline in the numberof banks is not due to exit from banking. Despite the nancial crisis,the exit rate the percentage of active banks that disappeared due to

We would like to thank Huberto Ennis, Joe Johnson, Thomas Lubik, and JohnWeinberg for helpful comments. The views expressed here are those of the authorsand not necessarily those of the Federal Reserve Bank of Richmond or the FederalReserve System. E-mail: Edward.Prescott@rich.frb.org.

1 There were even larger percentage declines in the number of savings institutions(savings and loans and savings banks) and credit unions. In 2007, there were 1,250 sav-ings institutions and 8,268 credit unions. In 2013, there were 936 savings institutionsand 6,687 credit unions, drops of 25 percent and 19 percent, respectively. (Sources:Savings institution numbers are from the Federal Deposit Insurance Corporation [FDIC]State Banking Performance Summaries. Credit union numbers are from NCUA Quar-terly Call Reports.)

24 Federal Reserve Bank of Richmond Economic Quarterly

failure or merger with another bank over the period 20082013 is notthat di¤erent from 20022007. There are signicant di¤erences in howbanks exited in the earlier period virtually all of the exit was due toacquisitions and mergers, while in the later period there were also manyfailures but mechanically it is the number of exits, not the reason forthem, that matters for calculating the total number of banks.

Instead, nearly two-thirds of the recent decline is due to the collapseof entry into commercial banking. Very few new banks have startedsince 2008 and most of these are thrifts or credit unions changing theircharter or, in a smaller number of cases, banks that were spun out ofmulti-bank holding companies. Entry by newly created banks, com-monly called de novo banks, has been minimal and was actually zeroin 2012. This is unprecedented over the last 50 years. Even during theprevious banking crisis of the late 1980s and early 1990s when largenumbers of banks failed or merged, there was still substantial entry.

The recent lack of entry has large implications for the number ofbanks and bank size distribution. Most new banks start small, sowithout that ow into banking, the number of small banks will decline.Indeed, we nd that the biggest drop is in the smallest size class, thosewith less than $100 million in assets, and that two-thirds of this declinecan be attributed to the lack of entry. This drop is of potential concernbecause small banks are considered to have a comparative advantage insmall business, relationship-type lending (Berger and Udell 2002). Forbetter or worse, a drastic change in the bank size distribution couldhave an impact on the allocation of credit to di¤erent sectors in theeconomy.2

To demonstrate the importance of entry for the future number ofbanks, we provide forecasts of the number of banks based on di¤er-ent assumptions about entry rates and show how these depend on thedegree to which entry recovers to historical rates. Finally, we discussvarious reasons for why entry has been so low.

1. DATA

Historically, in the United States there have been many legal and reg-ulatory limits on bank size. For example, in the 1960s banks couldnot branch across state lines, and in some states banks were requiredto be unit banks, that is, they could not even have a branch. Theselimits were removed gradually starting in the 1970s, more rapidly inthe 1980s, and mostly eliminated in the 1990s with the Riegle-Neal

2 Bank size distribution should have an e¤ect on bank productivity as well, but itis di¢ cult to measure bank productivity.

R. McCord and E. S. Prescott: Collapse of Bank Entry 25

Interstate Banking and Branching E¢ ciency Act of 1994. This lawallowed bank holding companies to acquire banks in di¤erent statesand allowed interstate bank mergers.3

A bank holding company is a company that directly or indirectlyowns at least 25 percent of a banks stock, controls the election of amajority of a banks directors, or is deemed to exert controlling inu-ence over a banks policy by the Federal Reserve (Spong 2000). Often,a bank holding company will have multiple banks or even anotherbank holding company under its control. Historically, this structurewas used to avoid some of the restrictions on bank branching (Mengle1990) while still allowing the bank holding company to jointly man-age many activities. For this reason, we follow Berger, Kashyap, andScalise (1995) and treat all banks and bank holding companies undera bank holding company as a single banking entity. For convenience,we will call one of these entities a bank.

Bank structure and bank size data are measured at the end of eachyear from 19602013. Data on bank structure are taken from the Fed-eral Reserves National Information Center bank structure database.We only include commercial banks and exclude savings and loans, sav-ings banks, and credit card banks.

Bank size data comes from the Reports on Condition and Income(the Call Report), which is collected by federal bank regulators.Bank size is measured by assets, though in a few places we use ad-ditional size measures. For the analysis, assets are also adjusted byo¤-balance sheet items starting in 1983. Starting in that period, banks,and larger banks in particular, began to undertake numerous activitieslike providing lines of credit, supporting securitizations, and issuingderivatives that expose a bank to risk but are not reported on a tradi-tional balance sheet. These adjustments signicantly increase the sizeof the largest banks. The Appendix contains more information on theseadjustments.

To facilitate comparison of bank size across years, we report sizemeasures relative to 2010 dollars. Data in other years are scaled bythe change in total bank assets between those years and 2010. Theresulting number is essentially a market share number, but scaled bythe size of the commercial banking industry in 2010. For example,total bank assets in 2000 were 50.5 percent of total bank assets in

3 See Jayaratne and Strahan (1997) for a history of bank branching restrictions andKane (1996) for a description of the Riegle-Neal Act.

26 Federal Reserve Bank of Richmond Economic Quarterly

Figure 1 Total Number of Independent Banks

Notes: All banks and bank holding companies that are under a higher-level hold-ing company are treated as a single independent bank. A more precise denitionof an independent bank is given in Section 1.

2010. Consequently, we roughly double the size of a bank in 2000 tomake it comparable to a bank in 2010.4

2. CHANGES IN BANK SIZE DISTRIBUTION

Figure 1 shows the number of banks from 1960 through 2013. Sev-eral distinct periods are apparent in the graph. From 1960 to 1980,the number of banks is relatively stable. There is a drop in the early1970s, which overlaps with the sharp recession of 19731975, but com-pared with future changes this drop is proportionally small. The mostdramatic changes start in 1980 and last through the late 1990s. This

4 An alternative way for scaling the data would be to use a price index like theconsumer price index. We do not use this measure because that price index was designedto measure changes in the price of goods and we are interested in changes to the sizeof a banks balance sheet, not what it charges to provide bank services. Furthermore,there have been much larger changes in total assets in the banking industry than inprice levels.

R. McCord and E. S. Prescott: Collapse of Bank Entry 27

Figure 2 Market Share of 10 Largest Commercial Banks

Notes: Market share of the 10 largest commercial banks for four di¤erent mea-sures. The number of employees is only reported starting in 1969 because theCall Report did not collect that information until then.

is the era when many regulatory restrictions were removed from bankbranching and interstate banking, and there was a commercial bankingcrisis in the 1980s and early 1990s when many banks failed. Thesefactors led to a large amount of consolidation through both merger andfailure. Starting in the late 1990s, however, the decline continues, butthe rate of decline slows down. This trend lasts until about 2005, beforethe crisis, and then the numbers begin to rapidly decline again.

A second phenomenon associated with the latter period of bankconsolidation is an increase in concentration, particularly for the largestbanks. Figure 2 shows the market share of the 10 largest banks forfour di¤erent measures of rm size. Interestingly, the big increase inconcentration starts around 1990 and continues until the nancial crisis,at which point it levels o¤.

Like many industries, the size distribution of banks consists of alarge number of small rms and a small number of large ones. Oneclass of distributions that is often used to t the size distribution of

28 Federal Reserve Bank of Richmond Economic Quarterly

Figure 3 Zipf Plot for Bank Size Distribution in 2013

rms is one that is based on a power law, that is, it satises the relation

f(x) = cx��;

where c > 0. Power laws also describe a large number of other empiricalphenomena in economics as well as in the natural sciences.5

In this article, we will look at the data with a Zipf plot, or rank-frequency plot. In our context, this means we rank banks by size andthen plot the log of the rank versus the log of the size of the bank. Ifthis relationship is linear, then it satises a power law because

yr = cr��;

where r is the rank of a bank measured by size and yr is the size of therth largest bank. Furthermore, when � = 1 (or is close to it), the datais said to satisfy Zipfs Law, that is, size is inversely proportional torank. In other words, the largest bank would be twice the size of thesecond-largest bank, three times the size of the third-largest bank, etc.

5 For a description of the use of power laws in economics, see Gabaix (2009). For adiscussion of their use to applications as diverse as word frequency, population of cities,and earthquake strength, see Newman (2005). For examples of their application to rmsize, see Axtell (2001) and Luttmer (2007).

R. McCord and E. S. Prescott: Collapse of Bank Entry 29

Table 1 Ten Largest Banks

Bank 2007 Bank 2013(billions) (billions)

JP Morgan Chase 2,503 JP Morgan Chase 2,518Bank of America 2,096 Bank of America 1,756Citigroup 1,824 Citigroup 1,614Wachovia 904 Wells Fargo 1,519Bank of New York Mellon 823 Bank of New York Mellon 600State Street 708 State Street 523Wells Fargo 580 U.S. Bancorp 386U.S. Bancorp 290 PNC 323HSBC Holdings 277 Capital One 298Northern Trust 258 Goldman Sachs 292

Notes: Size of the 10 largest banks measured by assets, expressed in 2010 dol-lars. The asset measure includes o¤-balance sheet conversions and only includesactivities under the banks charters.

Janicki and Prescott (2006) found that Zipfs Law did an excellent jobof tting the size distribution of banks in 1960 and 1970, but startingin 1980 it underpredicts the size of the largest banks.

Figure 3 shows the Zipf plot for 2013. The graph suggests thatZipfs Law still underpredicts the size of the largest banks and, fur-thermore, there are di¤erent ranges of the size distribution where banksize is proportional to rank, but these proportions di¤er along di¤erentsegments of the size distribution. Furthermore, it is obvious that thesize distribution of the smallest banks is poorly described by a powerlaw and therefore needs to be described by some other distribution.6

Interestingly, despite the severity of the nancial crisis, the Zipf plotfor 2007 (not shown) looks virtually identical to Figure 3. One reasonis that changes among the distribution of smaller banks are hard to seein the curve and, as we will see, there were signicant changes there.However, the other reason is that there were not signicant changes inconcentration among the largest banks. This is apparent in Figure 2,which shows that the market share of the 10 largest banks levels o¤after the crisis.

6 It is common in applications that the bottom part of the distribution is not welldescribed by a power law distribution, so scientists typically leave this part out of theiranalysis. For example, when looking at bank size distribution, Janicki and Prescott(2006) only consider the largest 3,000 banks when they assess how Zipfs Law ts thesize distribution of banks. Recent work by Goddard et al. (2014) develops a moregeneral formulation by tting a distribution in which there is a power law for the largestbanks, a lognormal distribution for small banks, and an endogenous cuto¤ between thetwo classes of banks. See also Goddard, Liu, and Wilson (2014) for an analysis of bankgrowth rates.

30 Federal Reserve Bank of Richmond Economic Quarterly

Table 2 Drop in Number of Banks by Size Class

Size Class(millions) 2007 2013 Change % Change< 100 2,538 1,771 �767 �30.2100500 2,706 2,634 �72 �2.75001,000 455 453 �2 �0.01,0005,000 338 333 �5 �1.55,00010,000 48 50 2 4.210,00050,000 39 44 5 12.8> 50,000 29 32 3 10.3Total 6,153 5,317 �836 �13.6

While the size distribution among the largest banks did not changemuch, there were signicant changes among the relative size of thelargest banks. Table 1 lists the size of the largest 10 banks in 2007 and2013. The top three largest banks did not change, but Wachovia ceasedto exist after being acquired by Wells Fargo. Northern Trust and HSBCexited the top 10 list, while PNC, Capital One, and Goldman Sachsentered it.

There are two features of these numbers worth noting. First, o¤-balance sheet activities have a large e¤ect on the size of some of theserms. For example, Wachovia is listed as having about $900 billionin assets in 2007. Nearly a third of that number ($269 billion) camefrom the o¤-balance sheet adjustments.7 See Appendix A for guresshowing how big this adjustment is for the banking sector as a whole.Second, by using Call Report data we are only measuring assets (ando¤-balance sheet assets) that are held under a bank holding companyscommercial bank charters.8 For some nancial institutions, this mat-ters. For example, most of Goldman Sachsactivities are done outsideits bank charter. In 2013, its balance sheet was about $912 billion (FRY-9C), which is much larger than the $292 billion reported in Table2. For others it is less important. A traditional commercial bank likeWells Fargo has most of its assets under its commercial bank charters.

The largest changes in the bank size distribution have occurredamong smaller banks, which is something that the Zipf plot does notshow that well. Consequently, we break banks into size classes andlook at the number of banks in each class. Table 2 reports these

7 The four largest o¤-balance sheet equivalents for Wachovia were unused loan com-mitments with an original maturity exceeding one year ($74 billion), securities lent ($59billion), derivatives ($50 billion), and nancial standby letters of credit ($40 billion).

8 For an analysis of how the activities of large bank holding companies have changedover the crisis, see Ennis and Debbaut (2014).

R. McCord and E. S. Prescott: Collapse of Bank Entry 31

Table 3 Drop in Number of Small Banks by Size Class

Size Class(millions) 2007 2013 Change % Change< 50 1,230 725 �505 �41.150100 1,308 1,046 �262 �20.0100200 1,407 1,357 �50 �3.6200300 687 678 �9 �1.3300400 359 372 13 3.6400500 253 227 �26 �10.3500750 290 296 6 2.17501,000 165 157 �8 �4.8

numbers. Not surprisingly, the biggest drop in the number of banks isin the smallest class of banks because the majority of banks are small.More interesting, however, is the percentage change. The biggest suchchange is in banks that hold less than $100 million in assets. The dropin this size class is about 30 percent in just ve years. This is an ex-traordinarily large decline. In the next three size classes, the numberof banks does not change that much, while there are increases in thethree largest categories.9

A closer look at banks that hold less than $1 billion further illus-trates that the smallest banks are disappearing. Table 3 breaks downthe size classes even further. There is an enormous drop of about 40percent in the number of banks that hold less than $50 million. In the$50$100 million range, there is a smaller, but still large, percentagedrop of 20 percent. Above $100 million, the change is more mixed.In some categories, the number of banks increases and in others itdecreases.

3. ENTRY AND EXIT

The recent decline in the number of banks shown in Figure 1 appearsto be a continuation of a trend that started around 1980 and, whenmeasured solely by the number of banks, that view would be correct.However, there is a signicant di¤erence from any previous period. Fig-ure 4 reports the number of entries and exits into commercial bankingexpressed as a fraction of the banking population.

9 To check the robustness of this result we also performed this analysis on othermeasures of bank size including on-balance sheet assets, deposits, and loans, both scaledand unscaled (nominal). Qualitatively, the results were similar for all these measuresexcept for scaled loans.

32 Federal Reserve Bank of Richmond Economic Quarterly

Figure 4 Banks that Enter and Exit by Fraction of TotalBanks

Notes: Entry and exit of banks expressed as a percentage of banks existing ineach year.

The most striking observation from Figure 4 is the unprecedentedcollapse of bank entry since 2009. Entry rates are on the order of 0.05percent, which is much smaller than the long-term average of 1.5 per-cent. Furthermore, as we will see in the next section, entry is actuallyweaker than these numbers indicate. The only period that is at all closeto this is 1993 and 1994, which followed the previous banking crisis andthe recession of the early 1990s.

The other striking observation from Figure 4 is that despite largenumbers of exits in di¤erent periods, like the mid-1980s and the mid-1990s, entry was usually strong. For example, in 1984, when more than5 percent of banks exited because of failure or merger, there were somany entrants that they equaled 3 percent of the banks that operatedat the beginning of that year. The late 1990s were similar. During themerger wave of that period, there was a lot of entry.

It is also apparent from Figure 4 that despite the nancial crisis,exit rates during the crisis are very similar to those from the 20022007pre-crisis period. The one signicant di¤erence between these periods

R. McCord and E. S. Prescott: Collapse of Bank Entry 33

Table 4 Commercial Bank Exit by Reason since 2002

Year Total Exits Failures Acquisition/Mergers2002 169 7 1622003 176 1 1752004 206 3 2032005 169 0 1692006 240 0 2402007 232 1 2312008 180 17 1632009 158 98 602010 195 126 692011 168 80 882012 181 37 1442013 171 18 153

Notes: Failed banks were obtained from the FDICs Historical Statistics on Bank-ing and then compared with our calculated list of exits. Banks that did not failwere treated as an acquisition/merger. Because we are measuring a bank at theholding company level and multiple failed banks can be part of the same holdingcompany, we report fewer failures than the FDIC.

is the reason for exit. Table 4 lists bank exits by reason from 20022013. Before the crisis, almost all exit was due to an acquisition ormerger while, during 20092010, failure was the most common reasonfor exit. Starting in 2011, failure accounts for about half of all exits,after which the rate of failure quickly declines.

The entry and exit rates demonstrate that the normal dynamics ofthe banking industry are not such that there is a xed stock of banksfrom which banks exit over time. Instead, it is of a dynamic industrywith lots of entry and exit in both good and bad economic times. Bythese perspectives, the collapse of entry is what is so striking about thelast few years.

4. A DEEPER LOOK INTO ENTRY (OR THE LACKTHEREOF)

A deeper look into the source of entry implies that entry in recentyears is actually weaker than the numbers suggest. In our data, wecan identify three distinct types of entry. First, there is a charterconversion, that is, a savings and loan, a savings bank, or a credit unionthat changes its charter to a commercial banking charter. Second, thereis a spino¤, which is a bank that was formerly part of a holding companybut has become independent. Third, there is a de novo entrant, whichis a newly formed bank.

34 Federal Reserve Bank of Richmond Economic Quarterly

Figure 5 Number of De Novo Entrants by Year

Notes: A de novo bank is a newly formed bank.

A de novo bank is a good measure of interest in entering bank-ing because it represents new capital, new management, and a neworganization. A charter conversion to a degree is just a relabeling ofan existing institution since there is overlap between the activities ofa commercial bank and other depository institution charters. Simi-larly, a spino¤ is just another way of legally organizing bank assets andmanagers that are already in the banking sector.

Figure 5 lists the number of de novo entries for each year since1960. The only two periods in which there is a sharp decline in thenumber of de novo banks are the early 1990s and the last few years.The former period coincides with the recession of the early 1990s andthe end of a commercial banking crisis, but de novo entry numbersquickly rebound. In contrast, the de novo entry numbers in the recentperiod are truly abysmal. In 2011, there were three de novo banks;10

in 2012, there were zero, and in 2013, there was only one. This last

10 These three banks were Alostar, Cadence, and Certusbank, which were all formedto acquire failed banks.

R. McCord and E. S. Prescott: Collapse of Bank Entry 35

Figure 6 Number of Spino�s by Year

Notes: A spino¤ is a newly independent bank that used to be part of a bankholding company. We identify a spino¤ by taking the bank ID of each new entrantand seeing if that ID was a bank that was in a holding company in the previousyear.

one was Bank of Bird-in-Hand, which was formed in Lancaster County,Pa., to serve the Amish community.

Spino¤s are unusual and to our knowledge have not been studiedin the banking literature. There are several reasons for why a bankholding company might undertake one. One reason is that a bankholding company might sell one of its healthy bank charters to outsideinvestors because the holding company is in nancial trouble. For ex-ample, in 2012 the bank holding company Capital Bancorp sold severalof its banks to local investors while it led for Chapter 11 bankruptcy(Stewart 2012). A second reason is that management thinks the bankwill be better managed separately rather than jointly. For example, in2005 Midwest Bank Holdings sold one of its subsidiaries, Midwest Bankof Western Illinois, to local managers and investors because the banksagricultural lending focus did not t well with the holding companysChicago growth strategy (Jackson 2005).

36 Federal Reserve Bank of Richmond Economic Quarterly

Table 5 Commercial Bank Entry by Type since 2002

Year De Novo Spino¤ Conversion2002 74 10 62003 90 7 102004 104 13 122005 132 8 32006 147 9 52007 140 6 82008 72 4 102009 38 0 02010 7 16 32011 3 6 122012 0 5 202013 1 3 11

Figure 6 reports the number of spino¤s by year for our data set. Ingeneral, spino¤s are unusual, though there was a spike in the mid-1980sand there were 16 in 2009.

The nal type of entry that we can identify is a charter conversion.A depository institution may want to switch charters because it wantsto expand certain types of lending (e.g., savings and loans and creditunions face limits on the type of lending that they do). Table 5 showsentry by type since 2002, and this makes clear that most entries since2011 came from charter conversions.

5. DECOMPOSING THE DROP IN THE NUMBEROF BANKS

The two trends we have identied the decline in the number of smallbanks and the collapse of entry are related. As we emphasized ear-lier, the dynamics of bank growth matter for the size distribution. Inparticular, the pool of small banks changes over time. Some grow toa new size class and some exit. These factors alone would reduce thenumber of small banks, so the ow into this pool matters a lot. For thesmallest class of banks, de novo banks are a critical part of the ow in.Many of these banks start small, so they replenish the stock of smallbanks, even as other ones are leaving that class.

We can get a sense of just how much the recent decline in smallbanks is due to the drop in bank entry by running a simple counterfac-tual. We break banks into the seven size classes of Table 2, calculatethe fraction of banks in each size class that move to another size class

R. McCord and E. S. Prescott: Collapse of Bank Entry 37

Table6AnnualTransitionProbabilitiesBetweenSize

Classesfor2008{2013

SizeClass

100

500

1,000

5,000

10,000

(millions)

Exit

<100

500

1,000

5,000

10,000

50,000

>50,000

<100

0.03

0.91

0.06

0.00

0.00

00

0100500

0.03

0.02

0.93

0.02

0.00

00

05001,000

0.04

0.00

0.06

0.84

0.05

00

01,0005,000

0.04

0.00

0.01

0.04

0.90

0.01

00

5,00010,000

0.04

00

00.03

0.86

0.06

0.00

10,00050,000

0.03

00

00.01

0.04

0.92

0.01

>50,000

0.01

00

00

00

0.99

Notes:Entriesinboldreectthefractionthatstayinthesamesizeclass.Rowsmaynotaddto1duetorounding.

38 Federal Reserve Bank of Richmond Economic Quarterly

in each year, and then take the average over the 20082013 period.11

We then use these transition probabilities along with some counterfac-tual assumptions on entry to see what would have happened to thenumber of banks under more typical entry conditions.

Table 6 shows the average annual transition probabilities for the20082013 period. Each row takes all the banks in a given size classand reports the fraction of them that are in each size class in thesucceeding year. For example, of banks that are less than $100 million,3 percent exited, 91 percent stayed in the same size class, and 6 percentmoved up to the next highest size category.12

For our counterfactual experiment, we take the number of banksin each size category in 2007 (column 2 in Table 2) and multiply thisby the transition probabilities. For entry, we take the average entryrate over the 20082013 period and for the counterfactual part weadd enough additional entrants so that the number of entrants equals129, which was the average number of new entrants over the 20022007 period. We put these entrants into size categories in the sameproportion as new entrants during the 20022007 period.13

Table 7 reports the number of banks in each size category for 2013and the number that would have existed under the counterfactual as-sumption on entry. It also lists the di¤erence, expressed in absoluteand percentage terms. With the counterfactual entry, there would havebeen 567, or 10.7 percent, more banks. There would still be fewer banksthan in 2007, when there were 6,153, but a lot more than the actual5,317 in 2013. The actual number of banks dropped in this periodby 836, while under the counterfactual the number would have onlydropped by 269. This means that the weaker entry accounts for therest of the drop, which is about 68 percent of the total.

Among size classes, the biggest di¤erence among banks is in theless than $100 million size class. In the counterfactual, there are 22percent more banks. Much of this di¤erence is directly accounted forby the lack of entry. Under the counterfactual entry assumptions, 129banks enter per year, and most of them enter the smallest size class.Furthermore, in each year, 91 percent of those new entrants stay in thisclass, so over time new entry adds a lot of banks to this size class.

11 See Adelman (1958), Simon and Bonini (1958), and Janicki and Prescott (2006)for more information about transition probabilities and how they can be used to assessthe dynamics of an industry.

12 Appendix B contains some more analysis of the transition matrix.13 In the 20022007 period, 81 percent of new entrants started in the under $100

million size category, 15 percent started in the $100$500 million size category, 2 percentstarted in the $500$1,000 million size category, and 2 percent started in the $1,000$5,000 million size category.

R. McCord and E. S. Prescott: Collapse of Bank Entry 39

Table 7 Number of Banks by Size Class with CounterfactualEntry

Size Class Data Counterfactual(millions) 2013 2013 Di¤erence % Di¤erence< 100 1,771 2,276 505 28.5%100500 2,634 2,711 77 2.9%5001,000 453 448 �5 �1.1%1,0005,000 333 329 �4 �1.2%5,00010,000 50 48 �2 �4.0%10,00050,000 44 40 �4 �9.1%> 50,000 32 32 0 0.0%Total 5,317 5,884 567 10.7%

Notes: Number of banks in each size class in 2013 compared with numbers underthe counterfactual assumption that the number of entrants in 20082013 is thesame as in 20022007.

6. WHAT ACCOUNTS FOR THE LACK OF ENTRY?

The literature on bank entry has identied three main factors that arepositively correlated with bank entry. The rst is that entry is morelikely in fast-growing, protable, and concentrated markets (Dunham1989; Moore and Skelton 1998), presumably because potential protsare higher in this type of market. The second is that entry is morelikely after recent mergers (Dunham 1989; Keeton 2000; and Bergeret al. 2004). Starting a bank requires experienced bankers and thereare more people available after a merger since mergers often involvelayo¤s.14 The third factor is that entry is more likely when regulatoryrestrictions on entry are relaxed (Ladenson and Bombara 1984; Lindleyet al. 1992), presumably because any decrease in entry cost will makeit more protable for a potential bank to enter.

Analysis and discussion of the recent lack of entry have focused onthe poor economic conditions and the increase in regulatory compli-ance costs. The recent economic recovery has been very weak, whichhas certainly reduced the potential return from entering. Adams andGramlich (2014) examine entry at the county level with an orderedprobit model estimated on U.S. data from 1976 to 2013. Based on thismodel, they conclude that 75 percent to 80 percent of the decline inbank entry over the last few years is due to low interest rates and a lackof demand for banking services. They point out that community bankprots are heavily dependent on the net interest margin, that is, the

14 At a longer time horizon, an industry with frequent mergers may create an in-centive to start a bank with the goal of selling it in the future.

40 Federal Reserve Bank of Richmond Economic Quarterly

Figure 7 Non-Interest Expense as a Percentage of Assets forBanks with Less $1 Billion in Assets and with $1Billion to $10 Billion in Assets

Notes: Nominal value of expenses and assets are used.

spread between deposit rates and lending rates, and with present Fed-eral Reserve monetary policy pushing lending rates down, this marginis relatively small.

While these results are suggestive, they are far from denitive.There are plenty of periods where net interest margins declined, yetentry did not collapse. Morris and Regehr (2014) study the histori-cal pattern of net interest income in community banks after recessionssince the mid-1970s. They observe signicant drops in this revenuesource during all recessions and argue that the recovery in net interestincome after the recent recession is not that di¤erent from the 20012002 recession and is actually higher than in the 19811982 recession.Furthermore, as we showed in Figure 4, entry rates were much higherafter every earlier recession. Indeed, the Adams and Gramlich (2014)model includes a dummy variable for the post-crisis period (2010 andafter) that is also important for explaining the recent lack of entry.Their model also predicts that, even if the net interest margin and theeconomy recovered to 2006 levels, there would still be almost no entry.

R. McCord and E. S. Prescott: Collapse of Bank Entry 41

Figure 8 Legal Fees, Accounting, Auditing, Consulting, andAdvisory Expenses to Asset Ratio

Notes: Combined legal fees and expenses, accounting and auditing expenses, andconsulting and advisory expenses measured as a percentage of assets for bankswith less than $1 billion in assets and with $1 billion to $10 billion in assets.Nominal values of expenses and assets are used.

It seems then that while the net interest margin is important, theremay be other factors at work.

The other line of analysis is that regulatory costs are discouragingentry. There are two distinct, but often mixed together, argumentsused here. The rst argument is that the general increase in regulationsresulting from the implementation of the Dodd-Frank Act of 2010 havemade banking signicantly more costly by requiring more resources tobe used for complying with regulations and that, furthermore, thereare economies of scale in complying with these regulations.

Peirce, Robinson, and Stratmann (2014) surveyed community bank-ers about compliance costs. The bankers responded that their mediannumber of compliance sta¤ increased from one to two.15 Other thanfor the smallest banks, this is not a big increase in number of em-ployees, but there are other sources of compliance costs that could bereected in the non-interest expense category of the Call Report incomestatement.

15 For an analysis of potential increases in costs to community banks of hiring ad-ditional compliance sta¤, see Feldman, Schmidt, and Heineche (2013).

42 Federal Reserve Bank of Richmond Economic Quarterly

Figure 7 shows non-interest expense as a percentage of assets forbanks with less than $1 billion in assets and for those with $1 billionto $10 billion in assets. For the smaller class, this ratio did not changemuch between 2007 and 2013, and while it is higher for the largerclass, it is still lower than it was in 2000. If compliance costs are reallyincreasing, then they are being swamped by changes in other expenses.

The non-interest expense number does not break out expenses be-tween compliance and non-compliance costs, but starting in 2008 theCall Report added some subcategories of expenses, including costs re-lated to legal fees, auditing, consulting, and advisory expenses. Pre-sumably, some of these costs are related to the costs of complying withregulations. Figure 8 shows these costs measured as a percentage ofassets for banks with less than $1 billion in assets.

There is an increase in these expenses from 2008 to 2011, but theincrease is relatively small and, more importantly, the size of theseexpenses is just too small to have a big e¤ect on bank protability. Forexample, entirely eliminating these expenses would only increase thereturn on assets by 10 basis points.

Based on this data, if regulatory costs are signicantly impactingbank expenses and protability, it is because other costs are decliningto o¤set the increase or regulatory costs are a¤ecting the operations ofbanks in such a way that less revenue is being generated. For example,many community bankers say that their leaders spend a lot of theirtime reading, interpreting, and reacting to the rules, and that for smallbanks, in particular, this pulls them away from things like making loansand managing their sta¤.16 This kind of cost is not something we canmeasure in the Call Report data.

The second argument related to regulatory change is that the costsof entry have increased due to regulations. To start a bank in theUnited States, organizers are required to get a banking charter fromeither a state or the federal government and to obtain deposit insurancefrom the FDIC. Once the organizers pass these hurdles, the de novobank is under heightened supervision for a period of time. One wayin which these costs have gone up is that the intensity of supervisionof newly chartered banks has increased. In 2009, the FDIC raised theperiod from three to seven years under which FDIC-supervised, newlyinsured depository institutions are subject to higher capital require-ments and more frequent examinations. Furthermore, FDIC approvalis now required for changes in business plans during this seven-yearperiod (Federal Deposit Insurance Corporation 2009).

16 Personal conversations with bankers by the second author.

R. McCord and E. S. Prescott: Collapse of Bank Entry 43

Figure 9 Trends

Notes: Forecasts of the future number of banks assuming existing transition ratesbetween size classes and with existing entry rates and historical entry rates.

A second way in which these entry costs may have gone up is thatthe application process has lengthened, become more rigorous, andgotten more expensive. There have been so few de novo banks the lastfew years that there is not much direct data on this cost. However,organizers of the one de novo bank in 2013 claim that the applicationprocess was signicantly longer and more intensive than in the past(Peters 2013).

7. LOOKING AHEAD

The future number of banks will depend on the conditions under whichbank entry rates recover. If the main reason for the lack of entry isthe low net interest margin, then entry numbers should recover whenthe economy improves and the Federal Reserve raises interest rates. Ifregulatory costs are the main reason for the lack of entry, then it willdepend on how these change over time.

Regardless of the reason for the lack of entry, until entry recovers(and assuming that exit does not decrease) the number of banks will

44 Federal Reserve Bank of Richmond Economic Quarterly

Figure 10 Total Banking Assets With and WithoutO�-Balance Sheet Adjustment

continue to decline. To illustrate how this drop could be a¤ected bychanges in entry rates, we ran two experiments similar to the counter-factual that we ran earlier. In both, we divided the banking industryinto the same seven size categories we used earlier. Like in the earliercounterfactual experiment, we took the annual transition probabilitiesbetween size categories, exit rates from each category, and the entryrate for the 20082013 period. We then took the size distribution ofbanks in 2013 and calculated what the number of banks would be in 10years if these transition rates did not change. We then took the sametransition probabilities and only raised the entry rate to match the his-torical average of 1.5 percent and then calculated what the number ofbanks would be in 10 years under this more typical entry rate.17

Figure 9 shows the number of banks through 2013 and then the twodi¤erent forecasts. While both forecasts predict a continued declinein the number of banks, there is a substantial quantitative di¤erence

17 There are obvious limitations to this exercise. In particular, entry and exit de-cisions are determined simultaneously in a market. Nevertheless, we think this simpleexercise is useful because exit rates did not change that much from before the crisis toafter it, so this assumption is plausible.

R. McCord and E. S. Prescott: Collapse of Bank Entry 45

Table 8 O�-Balance Sheet Items and Credit Equivalents asof 2013

Item Conversion FactorFinancial Standby Letters of Credit 1.00Performance and Standby Letters of Credit 1.00Commercial Standby Letters of Credit 0.20Risk Participations in BankersAcceptances 1.00Securities Lent 1.00Retained Recourse on Small Business Obligations 1.00Recourse and Direct Credit Substitutes 1.00Other Financial Assets Sold with Recourse 1.00Other O¤-Balance Sheet Liabilities 1.00Unused Loan Commitments (maturity >1 year) 0.50Derivatives

Notes: Conversion factors used by regulators for determining credit equivalentsof o¤-balance sheet items in 2013. The source is FFIEC 041 Schedule RC-Rwww.¢ ec.gov/forms041.htm. Credit equivalents for derivatives do not have a di-rect conversion factor but instead are based on the current and future possiblecredit exposure.

between them. The number of banks under the existing trend dropsanother 1,000 banks over 10 years, while it only drops by about 500banks under historic entry rates.

8. CONCLUSION

Since the nancial crisis began, the biggest change to the size distrib-ution of banks has been the decline in the number of small banks. Wedocument that much of this decline is due to the lack of entry. Wediscussed several reasons for why there might be less entry, includingmacroeconomic conditions, regulatory costs, and regulatory barriers toentry. Regardless of the reasons for the decline, however, it is clear thatto a large degree the number of banks as well as the size distributionof banks in the future will depend on whether entry recovers.

APPENDIX A: OFF-BALANCE SHEET ITEMS

Banks can make commitments that are not directly measured by a tra-ditional balance sheet. For example, a loan commitment is a promiseto make a loan under certain conditions. Traditionally, this kind of

46 Federal Reserve Bank of Richmond Economic Quarterly

promise was not measured as an asset on a balance sheet. As doc-umented by Boyd and Gertler (1994), providing this and other o¤-balance sheet items has become an important service provided by banks,which means that traditional balance sheet numbers do not accuratelyreport some of the implicit assets and liabilities of a bank.

We account for loan commitment and other o¤-balance sheet itemslike derivatives by converting them into credit equivalents and thenadding them to on-balance sheet assets. We use the weights used byregulators to determine credit equivalents for capital purposes. Someof these adjustments are made starting in 1983, but many are addedin 1990. The weights as of 2013 are reported in Table 8. Figure 10demonstrates the importance of the adjustment starting in 1990 byplotting aggregate assets and loans with and without the adjustment.

APPENDIX B: TRANSITION MATRIX

One interesting thing that can be done with the transition matrix isto calculate the steady-state distribution of bank size. If the size dis-tribution at time t is vector st and P is the transition matrix (alsocommonly called a Markov matrix), then the size distribution at t+ nis

st+n = Pnst:

If the transition matrix has the property that a bank starting inany category has a positive probability of moving to any other sizecategory in a nite number of steps, then several theorems can beproven. In particular, there exists a unique stationary size distribution,that is, there exists s, such that s = Ps. Furthermore, regardless ofthe initial distribution, the size distribution will converge to this uniquedistribution.

For the transition matrix in Table 6, Table 9 shows the stationarydistribution. There is a large fraction of banks in the over $50 billionsize category. The reason for this concentration is that in the transitionprobabilities over the 20082013 period, 99 percent of banks in thelargest size category stayed in it each year. Consequently, if a bankenters this category, it is very unlikely to leave, so banks accumulatethere. In the recent period, this reects the lack of merger activityamong the largest banks and that the largest banks were preventedfrom failing by the federal government during the crisis. In past periods,transition probabilities for the largest size class were very di¤erent. For

R. McCord and E. S. Prescott: Collapse of Bank Entry 47

Table 9 Stationary Distribution based on TransitionProbabilities between Size Classes for 2008{2013

Size Class Stationary Distribution in(millions) Distribution 2013 Data< 100 0.23 0.33100500 0.45 0.505001,000 0.10 0.091,0005,000 0.10 0.065,00010,000 0.02 0.0110,00050,000 0.03 0.01> 50,000 0.06 0.01

Notes: Columns do not add to 1 due to rounding.

example, over the 20002005 period, only 91 percent of banks in thelargest size class stayed there.

The stationary distribution is useful for illustrating what directionthe transition probabilities are taking the size distribution. As a long-term forecast, however, it is less valuable. It can take many iterationsfor a distribution to converge to its stationary distribution (over 200in this case) and, as Janicki and Prescott (2006) show, properties oftransition matrices for the banking industry have changed several timesover the last 50 years.

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